English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Monkeypox virus is emerging slowly with the decline of COVID-19 virusinfections around the world. People are afraid of it, thinking that it wouldappear as a pandemic like COVID-19. As such, it is crucial to detect themearlier before widespread community transmission. AI-based detection could helpidentify them at the early stage. In this paper, we aim to compare 13 differentpre-trained deep learning (DL) models for the Monkeypox virus detection. Forthis, we initially fine-tune them with the addition of universal custom layersfor all of them and analyse the results using four well-established measures:Precision, Recall, F1-score, and Accuracy. After the identification of thebest-performing DL models, we ensemble them to improve the overall performanceusing a majority voting over the probabilistic outputs obtained from them. Weperform our experiments on a publicly available dataset, which results inaverage Precision, Recall, F1-score, and Accuracy of 85.44\%, 85.47\%, 85.40\%,and 87.13\%, respectively with the help of our proposed ensemble approach.These encouraging results, which outperform the state-of-the-art methods,suggest that the proposed approach is applicable to health practitioners formass screening.

Monkeypox virus detection using pre-trained deep learning-based approaches